Continued dimensional scaling of CMOS processes is approaching fundamental limits and therefore, alternate new devices and microarchitectures are explored to address the growing need of area scaling and performance gain. New nanotechnologies, such as memristors, emerge. Memristors can be used to perform stateful logic with nanowire crossbars, which allows for implementation of very large binary networks that can be easily reconfigured. This research involves the design of a memristor-based massively parallel datapath for various applications, specifically SIMD (Single Instruction Multiple Data) like architecture, and parallel pipelines. The dissertation develops a new model of massively parallel memristor-CMOS hybrid datapath architectures at a systems level, as well as a complete methodology to design them. One innovation of the proposed approach is that the datapath design is based on space-time diagrams that use stateful IMPLY gates built from binary memristors. This notation aids in the circuit minimization in logic design, calculations of delay and memristor costs, and sneak-path avoidance. Another innovation of the proposed methodology is a general, new, architecture model, MsFSMD (Memristive stateful Finite State Machine with Datapath) that has two interacting sub-systems: 1) a controller composed of a memristive RAM, MsRAM, to act as a pulse generator, along with a finite state machine realized in CMOS, a CMOS counter, CMOS multiplexers and CMOS decoders, 2) massively parallel, pipelined, datapath realized with a new variant of a CMOL-like nanowire crossbar array, MsCMOL (Memristive stateful CMOL), with binary stateful memristor-based IMPLY gates. Next contribution of the dissertation is the new type of FPGA. In contrast to the previous memristor-based FPGA (mrFPGA), the proposed MsFPGA (Memristive stateful logic Field Programmable Gate Array) uses memristors for memory, connections programming, and combinational logic implementation. With a regular structure of square abutting blocks of memristive nanowire crossbars and their short connections, proposed architecture is highly reconfigurable. As an example of using the proposed new FPGA to realize biologically inspired systems, the detailed design of a pipelined Euclidean Distance processor was presented and its various applications are mentioned. Euclidean Distance calculation is widely used by many neural network and associative memory based algorithms.
| Identifer | oai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-3966 |
| Date | 06 June 2016 |
| Creators | Rahman, Kamela Choudhury |
| Publisher | PDXScholar |
| Source Sets | Portland State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations and Theses |
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